Method and system for bio-metric voice print authentication

ABSTRACT

A method ( 700 ) and system ( 900 ) for authenticating a user is provided. The method can include receiving one or more spoken utterances from a user ( 702 ), recognizing a phrase corresponding to one or more spoken utterances ( 704 ), identifying a biometric voice print of the user from one or more spoken utterances of the phrase ( 706 ), determining a device identifier associated with the device ( 708 ), and authenticating the user based on the phrase, the biometric voice print, and the device identifier ( 710 ). A location of the handset or the user can be employed as criteria for granting access to one or more resources ( 712 ).

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of priority to U.S.Provisional Application No. 60/685,427 filed on May 27, 2005 and Utilitypatent application Ser. No. 11/420,190 filed on May 24, 2006, which arehereby incorporated by reference.

FIELD OF THE INVENTION

The present invention relates, in general, to speech recognition and,more particularly, to voice identification.

BACKGROUND

Advances in electronic technologies and software have enabled systems tomore effectively recognize and identify people. For example, imageprocessing systems such as cameras can capture an image of a person andidentify a person from the image. Fingerprint scanning systems cancapture a fingerprint for identifying a person through touch. Voiceprocessing systems can identify a person through their voice. Thesetechnologies provide for identification of a user prior to use forensuring system security and delegating access to the system.

Voice Identification (ID) systems have been used in a variety ofsecurity-related applications. Voice ID, sometimes called voiceauthentication, is a type of user authentication that uses voiceprintsand pattern recognition software to verify a speaker. An adaptation ofbiometrics, Voice ID relies on the premise that vocal characteristics,like fingerprints and the patterns of people's irises, are unique foreach individual.

More people can interact together on-line over the Internet through thecoupling of mobile devices and computers. Mobile devices are capable ofgoing on-line and establishing connections with other communicationsystems. Identifying a user of the mobile device is an important aspectfor providing secure access. However, the identity of a user of themobile device is not generally available. A need therefore exists forauthenticating a user.

SUMMARY

Embodiments of the invention concern a method for voice authenticationon a device. The method can include receiving one or more spokenutterances from a user, recognizing a phrase corresponding to the one ormore spoken utterances, identifying a biometric voice print from the oneor more spoken utterances of the phrase, determining a device identifierassociated with the device, and authenticating the user based on thephrase, the biometric voice print, and the device identifier. Avariability of the one or more spoken utterances can be determined forcreating the biometric voice print. The biometric voice print is a vocaltract configuration that is physically unique to a vocal tract of theuser. Upon authenticating the user, access can be granted to one or moreresources having a communication with the device. A location of thedevice or the user can be determined for granting access.

BRIEF DESCRIPTION OF THE DRAWINGS

The features of the system, which are believed to be novel, are setforth with particularity in the appended claims. The embodiments herein,can be understood by reference to the following description, taken inconjunction with the accompanying drawings, in the several figures ofwhich like reference numerals identify like elements, and in which:

FIG. 1 is a mobile communications environment in accordance with anembodiment of the inventive arrangements;

FIG. 2 is an exemplary illustration of a voice authentication systemdeployed within the mobile communication environment of FIG. 1 inaccordance with an embodiment of the inventive arrangements;

FIG. 3 is an exemplary implementation of the voice authentication systemof FIG. 2 in accordance with an embodiment of the inventivearrangements;

FIG. 4 is a voice authentication system in accordance with an embodimentof the inventive arrangements;

FIG. 5 is a flowchart for creating a user profile suitable for use in avoice authentication system in accordance with an embodiment of theinventive arrangements;

FIG. 5 is a flowchart for verifying a user suitable for use in a voiceauthentication system in accordance with an embodiment of the inventivearrangements;

FIG. 6 is a flowchart for creating a user profile suitable for use in avoice authentication system in accordance with an embodiment of theinventive arrangements;

FIG. 7 is a method 700 for voice authentication on a device inaccordance with an embodiment of the inventive arrangements;

FIG. 8 is a voice authentication algorithm in accordance with anembodiment of the inventive arrangements; and

FIG. 9 is a voice authentication system in accordance with an embodimentof the inventive arrangements.

DETAILED DESCRIPTION

Detailed embodiments of the present method and system are disclosedherein. However, it is to be understood that the disclosed embodimentsare merely exemplary, and that the invention can be embodied in variousforms. Therefore, specific structural and functional details disclosedherein are not to be interpreted as limiting, but merely as a basis forthe claims and as a representative basis for teaching one skilled in theart to variously employ the embodiments of the present invention invirtually any appropriately detailed structure. Further, the terms andphrases used herein are not intended to be limiting but rather toprovide an understandable description of the embodiment herein.

The terms “a” or “an,” as used herein, are defined as one or more thanone. The term “plurality,” as used herein, is defined as two or morethan two. The term “another,” as used herein, is defined as at least asecond or more. The terms “including” and/or “having,” as used herein,are defined as comprising (i.e., open language). The term “coupled,” asused herein, is defined as connected, although not necessarily directly,and not necessarily mechanically. The term “suppressing” can be definedas reducing or removing, either partially or completely. The term“processing” can be defined as number of suitable processors,controllers, units, or the like that carry out a pre-programmed orprogrammed set of instructions.

The terms “program,” “software application,” and the like as usedherein, are defined as a sequence of instructions designed for executionon a computer system. A program, computer program, or softwareapplication may include a subroutine, a function, a procedure, an objectmethod, an object implementation, an executable application, a sourcecode, an object code, a shared library/dynamic load library and/or othersequence of instructions designed for execution on a computer system.

Embodiments of the invention concern a system and method forauthenticating a user. The method can include receiving one or morespoken utterances from a user, recognizing a phrase corresponding to oneor more spoken utterances, identifying a biometric voice print of theuser from a variability of one or more spoken utterances of the phrase,determining a device identifier associated with the device, andauthenticating the user based on the phrase, the biometric voice print,and the device identifier.

Embodiments of the invention also include an authentication system thatcan be based on a user's unique voice print, a phrase the user speaksduring a creation of the voice print, and a user's handset's identifier,for example an IMEI number. In one implementation a location of thehandset or the user can be employed as an additional criteria forapproving access to one or more resources. The system can replace, forexample, the current “social security number/mother's maiden name” modelof user identification with a more robust method using a biometriccharacteristic, namely, the user's voice.

Referring to FIG. 1, a mobile communication environment 100 for voiceauthentication is shown. The mobile communication environment 100 caninclude a voice authentication server 130, a database 130, and one ormore mobile devices 102. User profiles can be stored on the database 130which can be used to identify a user of the mobile device 102. A userprofile can include a pass phrase, a biometric voice print, and a deviceidentifier. The server 130 can compare a user's profile to other userprofiles stored on the database 140 for authorizing the user's voice.For example, a user of the mobile device 102 can speak into the mobiledevice for accessing one or more resources available to the mobiledevice. Upon authorizing the user's voice, access can be granted to oneor more resources. For example, a resource can be a server, a PBX, orany other suitable communication system. The resource can provide afeature or service available to the device such as a music downloading,on-line gambling, subscription, gaming, and the like. The resource canprovide access to a secure or non-secure website such as a personalinformation, a remote server, or a data store hosting financial data orbusiness data, but is not herein limited to these.

The server 130 can acknowledge whether a pass phrase spoken by the useris a correct pass phrase and whether the biometric voice printassociated with a pronunciation of the phrase is a correct match to auser profile in the database. In particular, the biometric voice printis captured by analyzing one or more variabilities in the user'sspeaking style during one or more pronunciations of the pass phrase. Forexample, the voice authentication server 130 can determine whethercharacteristics of the user's voice captured during a pronunciation ofthe pass phrase match one or more biometric voice prints in the database140 for authenticating access to one or more resources. The server 130can also verify that the mobile device 102 is a device authorized foruse to access resources and is a device associated with the biometricvoice print of the user. In particular, the server 130 can validate thatthe user speaking into the mobile device 102 is associated with themobile device. In one example, the server 130 can determine if thedevice is registered to the user through an IMEI number associated withthe captured biometric voice print. The IMEI number is a deviceidentifier that is unique to the mobile device. In another arrangement,the server 130 can determine a location of the device 102 forauthorizing access to one or more resources. For example, the mobiledevice 102 can include a global positioning system (GPS) for identifyinga location of the device. Alternatively, the server can authorize accessto resources based on a location stated by the user. For example, theuser can speak their location, and the server 130 can determine if thespoken location corresponds with an authorized or accepted location ofthe device or the user. The user's voice can be processed on the mobiledevice 102 or at the server 130 for validating an identity of the user.

The mobile communication environment 100 can provide wirelessconnectivity over a radio frequency (RF) communication network or linkwith one or more voice authentication servers 130 on the system. Theserver 130 can be a Gateway, PBX, or any other telecommunicationsnetwork device capable of supporting voice and data delivery.Communication within the network 100 can be established using awireless, copper wire, and/or fiber optic connection using any suitableprotocol (e.g., TCP/IP, HTTP, HTTPS, SIP etc.). In one arrangement, themobile device 102 can communicate with a base receiver 110 using astandard communication protocol such as CDMA, TDMA, OFDM, GSM, or thelike. The base receiver 110, in turn, can connect the mobile device 102to the Internet 120 over a packet switched link. The internet 120 cansupport application services and service layers for providing media orcontent to the mobile device 102. Application service layers can includedatabase access for financial or business based applications. The mobiledevice 160 can also connect to other communication devices through theInternet 120 using a wireless communication channel. The mobile device160 can establish connections with a server 130 on the network and withother mobile devices for exchanging voice, data, and media. The servercan host application services directly, or over the internet 120 whichcan be accessed through the mobile device 102.

The mobile device 102 can send and receive data to the server 130 orother remote servers on the mobile communication environment 100. Forexample, the mobile device 160 can also connect to the Internet 120 overa WLAN. Wireless Local Access Networks (WLANs) provide wireless accessto the mobile communication environment 100 within a local geographicalarea. WLANs are typically composed of a cluster of Access Points 104also known as base stations. The mobile communication device 102 cancommunicate with other WLAN stations such as the laptop 103 within thebase station area for exchanging voice, data, and media. In typical WLANimplementations, the physical layer uses a variety of technologies suchas 802.11b or 802.11g WLAN technologies. The physical layer may useinfrared, frequency hopping spread spectrum in the 2.4 GHz Band, ordirect sequence spread spectrum in the 2.4 GHz Band.

The mobile device 102 can send and receive data to and from the server130 over a circuit switch RF connection 110 or a packet based WLAN AP104, but is not herein limited to these. Notably, the data can includethe user's profile which can be shared amongst one or more voiceauthentication servers for granting the user access to one or moreresources. Understandably, voice can be represented as packets of voicewhich can be transmitted to and from the mobile devices 160 to providevoice communication. For example, a user of the mobile device 160 caninitiate a call to the server 130 or the laptop 103 for accessing one ormore features available to the mobile device. Voice data can betransmitted over the mobile communications environment 100 therebyproviding voice communication. The mobile device 160 can be acell-phone, a personal digital assistant, a portable music player, orany other type of communication device.

Referring to FIG. 2, an exemplary illustration of a voice authenticationsystem 200 deployed within the mobile communication environment 100 isshown. The voice authentication system 200 can include the voiceauthentication server 130, an interface 150, and the database 140. Theserver 130 can access the database 140 through the interface 150 forretrieving user profiles. The interface can include a web layer 152, abusiness layer 154, and a database access layer 156. It should be notedthat the interface 150 is merely illustrative of the transport layersinvolved with data processing on a network. The interface 150 may havemore or less than the number of components shown and is not limited tothose shown.

The database 140 can include a plurality of user profiles 142 for voiceauthentication. A user profile 142 can be unique to the user and uniqueto the device. The user profile 142 can include a biometric voice print144, a pass phrase 146, and a mobile device identifier 148. The passphrase 146 can be one or more words specifically selected by the user tobe recited during voice authentication. When the user speaks the passphrase into the mobile device 102, a voice print of the user's voice canbe captured and stored in the user profile 142. The biometric voiceprint 142 identifies characteristics of the user's speaking style thatare unique to the user. In particular, the biometric voice print 142represents a vocal tract configuration difference that is physicallyunique to a vocal tract of the user. That is, the user's vocal tract iscapable of undergoing physical changes which are dependent on thephysical formations of the user's vocal tract. The biometric voice printcaptures the physical features associated with these characteristicchanges of the vocal tract during the pronunciation of the pass phrasethat are unique to the individual. A user's vocal tract configurationincludes the esophagus, the pharynx, the larynx, the mouth, the tongue,and the lips. These physical attributes can undergo a certain physicalchange during speech production during the articulation of a passphrase, which is characteristic of the user's vocalization and speakingstyle. In particular, the amount of change these physical attributesundergo during one or more pronunciations of a spoken utterance can bemeasured for validating a user's identity.

Referring to FIG. 3, an exemplary implementation 300 of the voiceauthentication system 200 is shown. The exemplary implementation 300,includes a handset 102, such as a mobile telephone or other mobilecomputing device, and a voice authentication server 130 in communicationwith the handset over the mobile communication environment 100. Theserver 130 can be any suitable computing or networking server. Thesoftware running on the server 130 can include the web layer 152 (SeeFIG. 2), for communication with the handset, a business layer (154), anda database access layer (154) for storing and retrieving data, thoughare not limited to these. The server 130 can also include a monitoringpage, which allows administrative access to the server. For example, auser can update their profile through the monitoring page. The voiceauthentication server 130 provides for user profile creation, userprofile maintenance, and user authentication. For example, a userprofile can be generated from the biometric voice print, the passphrase, and the device identifier and stored in the voice print database140 as described in FIG. 2. User profile maintenance entitles a user toupdate or change their profile details such as their biometricvoiceprint, and password, and associated information. Userauthentication allows users to be authenticated against their previouslycreated voiceprints. The authentication can be performed using theuser's recorded voice, and the handsets IMEI or the PIN provided to theuser. For example, in place of the IMEI, a PIN can be assigned to themobile device for associating the device with the user's profile.

In addition to the system components previously shown in FIG. 2, theexemplary implementation 300 can include a gateway 145 inserted betweenthe voice authentication server 130 and the existing call processingmobile communication environment 100 of FIG. 1. In one arrangement, theserver 130 can support subscriber compliance, LDAP, and audit trails. Inone arrangement, the gateway can 145 can verify a location of the callerusing information through GPS positional data provided by the mobiledevice 120. The combination of the biometric voiceprint recognition witha location verification capability makes a particularly convenientsolution for such applications as gambling (which may, for example, onlybe allowed in some states or territories), or commerce (where sale ofcertain items may not be allowed to some jurisdictions). The gateway 145can identify a location of the device from the GPS data to establish alocation of the caller.

The gateway 145 can also perform call matching and routing in the mobilecommunication environment 100. For example, as is known in the art, thegateway can support ANI and DNIS for identifying a calling number and acalled number associated with the user. A user can be identified by thenumber from which the user is calling, or by the number which the useris calling. In one arrangement contemplated, the calling information canbe included as part of the user profile and used to verify an identityof a user. In practice, the voice authentication server 130 canauthenticate a user speaking into the mobile device 160 with referenceto user profiles stored on the database 130 by inquiring the gateway 145for caller identification information and location information.

Referring to FIG. 4, an exemplary voice authentication system 200 isshown. The voice authentication system 200 can include the mobile device102 having a connection to the voice authentication server 130. Theauthentication server 130 can include an authentication servlet 420, aprofile management module 420, a verification module 420, and a voiceprint database 140. The modules may reside on the server 130 or atremote locations on other servers within the mobile communicationenvironment 100. FIG. 4 refers to a client-server based architecturethrough aspects of the invention are not limited to this configuration.The principles of voice authentication can be equally applied indistributed networks and peer to peer networks.

It should be noted that some of the components are carried forward fromFIG. 1 and that the components are provided merely to illustrate oneembodiment for integrating the voice authentication system 200 withinthe mobile communications environment 100 (See FIG. 1). In practice, thevoice authentication system 200 can grant a user of a mobile deviceaccess to one or more resources available to the device based on anauthentication of the user's voice for accessing the resources orservices. The voice authentication system 200 is not limited to theprogram modules shown or the architecture of the program modules. Theprogram modules are merely presented as one embodiment for deploying theinventive aspects of voice authentication described herein.

The voice authentication system 200 can include an application 410running on the mobile device (102). The application can be a softwareprogram written in a programming language such as C, C++, Java, VoiceXML, Visual Basic, and the like. For example, the application 410 can bea financial or business application for sending confidential or secureinformation to and from a secure website. The confidential informationcan be in the form of voice, audio, video, or data. The application 410can acquire access to underlying communication protocols supported bythe mobile device. For example, the application 410 can be a Java 2Micro Edition (J2ME) applet having socket connections supporting HTTP toone or more servers communicatively connected to the mobile device 410.The communication protocols can be supported through a native Cinterface. For example, the J2ME can access native C code on the mobiledevice 410 for connecting to a server (130).

The application 410 can communicate with an authentication servlet 420running on the voice authentication server 130 (See FIG. 1). TheAuthentication Servlet can act as a front end to the mobile deviceclient 102 and direct requests to the voice authentication server 130depending on request type. For example, the request type may be of auser profile creation, a user profile update, or a user profileauthentication as previously described. Based on the request type, theauthentication servlet 420 can invoke an appropriate profile managementfunction. That is, upon determining the request type, the profilemanagement module 420 can communicate with the application 410 toperform the associated request.

In one arrangement, the authentication servlet 420 and the application420 can communicate over a secure HTTP connection 412. Theauthentication servlet 420 can be communicatively coupled to averification module 430 for authorizing a user. In one arrangement, theauthentication servlet 420 can communicate with the verification module430 over a Java Native Interface (JNI) 414. The JNI 414 providesprogramming language translation between the program components. Forexample, the authentication servlet 420 can be written in Java, whereasthe verification module 430 may be written in C. The JNI 414 provides aninterface to transport data from one format to another while preservingstructural aspects of the code and data. The verification module 430 cancommunicate information to and from the application 410. Notably, themobile device 102, HTTPS 412, authentication servlet 420, and JNI 414establish a channel of communication between the verification module 420on the voice authentication server (130) and the application (410) onthe mobile device 102.

In practice, the mobile device 102 can send a user profile 142 (See FIG.2) to the verification module 420. For example, when a user desiresaccess to one or more resources or services offered to the mobiledevice, the mobile device 102 can present the application 410. Themobile device can also present the application when the user creates auser profile. For example, the application 410 can be a J2ME applicationwhich asks the user to speak a password phrase. The application 410 canalso access a device identifier on the mobile device 102 such as an IMEInumber. The information can be used to create the user profile. Incertain device, an IMEI number extraction mechanism may not be supportedthrough J2ME. Accordingly, such devices may include a provision for userto key in a short PIN which the user can easily remember and use forauthentication. If an IMEI number is not supported, the user may berequired to key in the PIN, which is then used to approve sending astored IMEI number.

In one arrangement, the mobile device 102 can include a speechrecognition engine for validating a pass phrase. Understandably, thevoice recognition engine may only evaluate that a phrase was recognized,and not an identity of the user. Accordingly, a first aspect of thevoice authentication can be performed on the mobile handset; that is,the verifying the pass phrase. The biometric voice print authenticationand device identifier can be evaluated at the server. Thus, a secondaspect of voice authentication can be performed at the server.

Alternatively, the entire voice authentication, including the speechrecognition, can be conducted on the server 130. In this case, theapplication 410 can create a user profile 142 (See FIG. 2) whichincludes the pass phrase (144), the biometric voice print (146), and theIDEI (148). Upon speaking the password phrase, the J2ME application 410can send the user profile to the verification server. In onearrangement, the J2ME application 410 can perform voice processing onthe spoken utterance (i.e. pass phrase) and encode one or more featuresof the biometric voice prior to create the user profile and sending itto the verification module 430. The encoding can compress the voice datato reduce the size of the voice packets required for transmitting thespoken utterance. For example, the voice data can be compressed using avocoder as is known in the art. In a second arrangement, the spokenutterance can be transmitted in an uncompressed format to theverification module 430. For example, the audio can be transmitted inpulse code modulation (PCM) format or Microsoft Wave Format (WAV).

The profile management module 420 can communicate with theauthentication servlet 420 for evaluating one or more user profilesstored in the voice print database 140. The profile management module420 can create, update and delete user profiles. The profile managementmodule 420 can also synchronize with other profile management systems.For example, the profile management module 420 can expose an API forintegration with external systems after successful authentication of auser. In one arrangement, the Application Programming Interface (API)allows application developers to quickly integrate their applications inaccordance with the aspects of voice authentication system hereindiscussed. For example, referring back to FIG. 2, the API can include amodule for creating the biometric voice print (144), a module forcreating the pass phrase (142), and a module for identifying the device(146). The API provides an interface to the authentication servlet 420for accessing voice-print creation and authentication services.

The profile management module 420 can communicate with the voice printdatabase 140 over a Java Database Connectivity (JDBC) 416 interface. TheJDBC 416 can provide data access for retrieving and storing data fromthe voice print database 140. For example, the voice print database 140can be a relational database composed of tables which can be indexed berow column formatting as is known in the art. The JDBC 140 provides astructured query language locating data headers and fields within thevoice print database 140. The profile management module 420 can parsethe user profile for the biometric voice print and compare the biometricvoice print with other voice prints in the voice print database 140. Inone arrangement, biometric voiceprints can be stored using the mobilehandsets' IMEI number for indexing. Notably, the voice print database140 includes one or more reference voice prints from multiple user'shaving a registered voice print. Upon determining a match with a voiceprint, the profile management module 420 can grant access to the user toone or more resource. For example, the profile management module 420 canallow a socket connection to one or more secure websites, businessdatabases, financial centers, and the like.

Referring to FIG. 5 a flowchart for user profile creation is shown. Theuser profile creation may contain more or fewer than the number of stepsshown. Reference will be made to FIG. 4 for describing the steps. Atstep 501, the user starts the application. For example referring to FIG.4, the user activates a J2ME application 410. Alternatively, the usermay be accessing a website, voice mail, or requesting a service thatrequires authentication, such as a log-in screen. In this case thedevice may automatically launch the J2ME application 410 for authorizingthe user. At step 502, the user is prompted to record his voice forvoice print creation. The user can submit a particular phrase that theuser will recite during voice authorization. At step 503, the userrecords their voice using the provided application (410). At step 504,the user can enter in a PIN number. Again, the PIN number may berequired if the application cannot retrieve an IMEI number from thedevice. If the application 410 can access the IMEI, then the PIN numbermay not be required. At step 505, the user is prompted to register hisprofile. For example, the user can elect to store the newly created userprofile on a voice print database for later retrieval. At step 506, theregistration details along with the recorded voice are sent to theauthentication server. At 507, The Authentication server (130) createsthe user's voiceprint. At step 508, the Authentication server (130)creates the user's profile using the user's voice print, and IMEI (orPIN). For example, the user profile can be stored on the voice printdatabase (140). At 509, the Authentication server (130) responds backwith a positive confirmation to the user.

Referring to FIG. 6, a flowchart for verifying a user through voiceauthentication 600 is shown. The authentication 600 may contain more orfewer than the number of steps shown. Reference will also be made toFIG. 4 for describing components associated with practicing the steps.At step 601, the user starts application. The application may also startautomatically based on a user's action, such as accessing a feature orservice that requires authentication. At step 602, the user is promptedto record his voice for voice print verification. This is the samephrase that was recorded during user profile creation 500. At step 603,the user records his voice using the provided application (410). At step604, the user types in the PIN that was used to register with theauthentication server during user profile creation 500. At step 605,authentication details along with the recorded voice are sent to theauthentication server (130). At step 606, the authentication serverretrieves the user's voiceprint using the user's PIN. At step 607, theauthentication server (130) uses the Verification module to verify theuser's recorded voice against one or more stored voiceprints. At step608, the authentication server responds back to the user. At step 609,if the authentication is successful, the user can proceed further withthe service or application. At step 610, If the authentication isunsuccessful the user is prompted about authentication failure and theapplication exits.

Referring to FIG. 7, a method 700 for voice authentication on a deviceis shown. The method can include receiving one or more spoken utterancesfrom a user (702), recognizing a phrase corresponding to the one or morespoken utterances (704), identifying a biometric voice print of the userfrom a variability of the one or more spoken utterances of the phrase(706), determining a device identifier associated with the device (708),and authenticating the user based on the phrase, the biometric voiceprint, and the device identifier (710). In particular, in onearrangement, the user speaks the spoken utterance (e.g. pass phrase)multiple times. The variation in the user's voice can be evaluated todetermine changes in the user's vocal tract configuration. In onearrangement, a location of the device or the user can be determined(712) for granting access as previously described in FIG. 3.

The vocal tract configuration changes can be captured in the biometricvoice print and compared with a plurality of reference voice prints on avoice print database for identifying a match. That is, a first voiceprint and at least a second voice print can be generated in response toa speaker's voice, a difference between the first voice print and asecond voice print can be identified, and a determination can be made asto whether the difference corresponds to a natural change of thespeaker's vocal tract. Notably, the biometric voice print is a vocaltract configuration that is physically unique to a vocal tract of theuser. Consequently, the speaker can be authenticated if the differenceis indicative of a natural change in the speaker's vocal tract.

For example, referring back to FIG. 3, the device 102 implementing thevoice authentication method 700 can establish a connection to at leastone authentication server, send a user profile to at least oneauthentication server, compare the user profile with a plurality ofreference profiles stored on the at least one authentication server, anddetermine if the user profile matches one of the plurality of referenceprofiles for authenticating the user. Upon recognizing the phrase, thevoice authentication server, or the device, can evaluate one or morevocal tract configuration differences between the spoken utterances. Oneor more vocal tract shapes from the plurality of reference profiles canbe matched based on the vocal tract configuration difference.

In the foregoing, a detailed description of the voice authenticationsystem for practicing the methods steps 700 is provided. In particular,referring to FIG. 8, an algorithm 800 for the voice authenticationaspect of the voice authentication system is presented. The algorithm800 is a high level description of the underlying voice processingmethods employed for validating an identity of a user based on biometricvoice print analysis. As such, it should be noted that the algorithm 800can contain more than or fewer than the number of steps shown. In fact,each step can contain further contain steps not shown in the drawingsbut herein set forth in the specification. Reference will be made toFIG. 4 when describing method 800.

At step 802, a speech utterance can be segmented into vocalized frames.For example referring to FIG. 4, the pass phrase (e.g. spoken utterance)the user speaks into the mobile device 102 can be partitioned intovoiced and unvoiced segments. That is, regions corresponding to periodicregions such as vowels can be classified as voiced, and regionscorresponding to non-periodic regions such as consonants can beclassified as unvoiced. At step 804, Linear Prediction Coding (LPC)coefficients can be calculated from the voiced regions and at step 806transformed into Linear Spectrum Pairs (LSP). LSP coefficients aresuitable for compression and coding. At step 808, formants can becalculated from the LSP coefficients. Formants are those portions of thespeech spectrum that correspond to resonances and nulls “formed” by thevocalization process. In particular, the physical structures of thehuman speech production system such as the throat, tongue, mouth, andlips form cavities which create resonances in the pressure waveemanating from the lungs. The formants in the spectral domain representcharacteristics of the users vocal tract formation during pronunciationof the voiced frames. At step 810, information regarding the formantstructure and features extracted during the LPC/LSP analysis can beincluded in a feature matrix. At step 812 the feature matrix can benormalized. One aspect of normalization can include removing backgroundnoise. A second aspect of normalization can include accounting for vocaltract configuration length and area. At step 814, a voice print andthreshold can be calculated from the feature matrix. The biometricvoiceprint can include the features shown in Table 1.

TABLE 1 1. A reference matrix. One of the feature marixes used forvoiceprint calculation 2. An adaptive distance threshold (logarithmicdistance, LD). 3. A variation bounds for each of the feature vectors inthe feature matrix. A variation bounds includes a maximum and minimumvalue for each value of the feature vector. 4. Two mean average vectorsthat are calculated from three feature matrixes. 5. A mean vectorcalculated by adding and averaging matrix rows. 6. A mean featurevariation vector. 7. A mean feature difference vector.

In practice, a user can present a spoken utterance corresponding to apass phrase that was used during voice enrollment; that is, when theuser registered their biometric voice print with a voice authorizationserver. For example, during enrollment, a user pronounces the same passphrase three times. A feature matrix is calculated for each recording ofthe pass phrase. The feature matrix is a matrix of numeric values thatrepresent features of the speaker's voice. In this case, three featurematrices are used to create the biometric voice print. For example, withreference to the enumerated voice print listed above in Table 1, variousfeatures including averages and bounds are used in the voice print. Thefeatures of Table 1 are used in conjunction with the three matrices todefine the voice print. For example, the feature matrices define thefeatures of the voice, and the attributes of Table 1 describe thevariation of a vocal tract configuration. For instance, the attributesof Table 1 represent a vocal tract shape. Notably, the variation in thepronunciation of the pass phrase is captured by identifying bounds ofthe feature vector for each voice frame which are defined in thebiometric voice print of Table 1. For example, index 3 of the biometricvoiceprint in Table 1 identifies a maximum and minimum value for eachelement of the one or more feature vectors. For instance, the bounds canidentify the naturally occurring change in amplitude of a formant, thechange in bandwidth of a formant, of the change in location of a formantduring pronunciation of the pass phrase, which is particular to a userspeaking the pass phrase.

During verification, the user speaks the same spoken utterancecorresponding to the pass phrase, and a biometric voice print isgenerated. The biometric voice print is compared against previouslystored voice prints for identifying a match. During the verificationprocess, a feature matrix is also calculated from the spoken phraseusing the voice authentication algorithm 800 as used in enrollment. Thisfeature matrix is compared against one or more reference matrices storein a voiceprint database. A logarithmic distance can be calculated foreach feature matrix of a biometric voice print. If the logarithmicdistance is less than a predetermined threshold level, a match can bedetermined, and the speaker can be identified. One unique aspect of theverification process includes setting a comparison threshold level thatdepends on a threshold from a voiceprint. The threshold depends onintra-speaker variability and can be adapted based on the user's voice.Alternatively the threshold can be set independently of the thresholdand which is not adapted based on the user's voce.

In one implementation, the method 800 of generating the voice print canbe performed by a handset, and the method 700 of authorizing a user canbe performed by a server in communication with the handset. Referring toFIG. 9, a diagram depicting various components of a voice authenticationsystem 900 for practicing the method 800 of generating the voice printis shown. The voice authentication system 900 can include a voiceprocessor 144 and a biometric voice analyzer 148. The voice processor144 can receive a spoken utterance and at least one repetition of thespoken utterance from a user. The biometric voice analyzer 146 cancalculate one or more vocal tract shapes from the spoken utterance andthe at least one repetition, and calculate a vocal tract configurationdifference between the one or more vocal tract shapes based on a varyingpronunciation of the spoken utterance and the at least one repetition. Avocal tract configuration difference corresponds to a bounded physicalchange of a user's vocal tract associated with one or more spokenutterances. For example, a vocal tract configuration difference can bebased on a momentum spectrum that accounts for a dynamic change of aspeech spectrum over time. The momentum spectrum can include a lowerbound and an upper bound for the one or more voice segments of speech,such that variations in the speech spectrum between the lower bound andthe upper bound correspond to a unique vocal tract configuration.

In one arrangement, though not required, the voice processor 144 caninclude a speech recognizer 146. The speech recognizer 146 can validatea phrase spoken by the user during voice authentication. In one aspect,the speech recognizer 146 can also identify voiced and unvoiced regionsin the spoken utterance, recognize one or more phonemes from the voicedregions, and identify a location of the one or more phonemes in thevocalized frames (e.g. voiced segments). The voice processor can segmenta spoken utterance into one or more vocalized frames, generate one ormore feature vectors from the one or more vocalized frames, calculate afeature matrix from the one or more feature vectors, and normalize thefeature matrix over the one or more vocalized frames. For example, afeature matrix can be calculated for every spoken phrase. The speechutterance can be partitioned into one or more speech frames having timelength between 5 and 20 ms.

The voice processor can identify an absolute minimum and maximum in thespeech frames. The values can be compared against a predeterminedthreshold. If both maximum and minimum values are less than an amplitudelevel then the frame can be classified as having no voice component andthe algorithm 800 proceeds to the next frame. If the minimum and maximumare greater than the amplitude level then an autocorrelation function iscalculated for the speech frame signal. If one or more pre-specifiedautocorrelation terms are less than a predefined threshold then theframe is considered to lack a voiced signal and the algorithm 800processed to the next frame.

A Fast Fourier Transform (FFT) can be applied to the voiced windowedspeech frame. The speech frame can be multiplied by a weighting windowto account for discontinuities prior to frequency analysis. The FFTconverts each frame of N samples from the time domain into the frequencydomain. The result obtained after this step is an amplitude spectrum orspectrum.

Human perception of the frequency contents of sounds of speech signalsdoes not follow a linear scale. Accordingly, a Bark scale can be appliedto the amplitude spectrum for converting from a linear frequency scaleto a scale that approximates human hearing sensitivity. That is, aperceptual filter bank analysis can be performed on the one or morevocalized frames. One approach to simulate the Bark frequency is to usefilter bank, one filter for each desired Mel-frequency component. Thefilter bank can have a triangular band pass frequency response. Thespacing as well as the bandwidth is determined by one bark-frequencyinterval. The number of Bark spectrum coefficients IBR depends onfrequency range. In telephone channel frequency range 3400 Hz matches 17Bark. Therefore 0-3400 Hz frequency range matches 17 one bark bandwidthfilters. Each filter band can have a triangular band pass frequencyresponse, and the spacing as well as the bandwidth can be determined bya constant bark frequency interval. The spectrum frequency shifted inaccordance with the Bark scale can be called a Bark spectrum.

The Bark spectrum X_(F)(n,k) can be multiplied by weighting factors on abark-scale frequency bank and the products for all weighting factors canbe summed to get an energy of each frequency band. An energy matrix canbe calculated for each speech frame of the spoken utterance. Forexample, the spoken pass phrase can be represented as a matrix E(m,i).In order to remove some undesired impulse noise, a three-point medianfilter can be used for smoothing. The smoothed energy E_(S)(m,i) can benormalized by removing the frequency energy of background noise to getthe primary energy associated with the speech signal E_(V)(m,i). In onearrangement, the background noise energy E_(n)(m,i). can be estimated byaveraging the energy of the first 8 speech frames.

E_(V)(m, i) = E_(S)(m, i) − E_(n)(i)${E_{n}(i)} = \frac{\sum\limits_{m = 1}^{8}\; {E_{S}( {m,i} )}}{8}$

With the smoothed and normalized energy of the i-th band of the m-thframe E_(V)(m,i), the total energy of the speech signal at the i-th bandcan be calculated:

${E_{B}(i)} = {\sum\limits_{m = 1}^{M}\; {{E_{V}( {m,i} )}}}$

A threshold can be calculated:

${T(i)} = {\ln \frac{E_{B}(i)}{E_{n}(i)}}$

If T(i)>1.5 the band can be left intact as more speech can be consideredpresent than noise. Conversely, it the threshold is less, the band canbe considered too noisy and not used in further calculations.Accordingly, higher speech content is reflected when more bands exceedthe 1.5 threshold. The bands exceeding the threshold can be counted asthe new band count. That is, the perceptual filter bank analysisincludes estimating speech energy and noise energy in one or morefrequency bands along a Bark frequency scale. Background noise can besuppressed during the perceptual filter bank analysis, by discardingfilterbanks having a ratio of speech energy to noise energy that do notexceed a threshold of vocalization. The total signal energy can becalculated with the new band count:

${E_{a}(m)} = {\sum\limits_{i = 1}^{IJQ}\; {{E_{V}( {m,i} )}}}$

A minimum and maximum value can be determined for each E_(a)(m). Anadaptive vocalized segmentation threshold can also be calculated basedon the determined minimum and a root mean square term.

Tv=E _(a)Min+0.3*RMS

RMS−standard deviation of Ea(m)

Frames having E_(a)(m)>Tv can be classified as vocalized and a newmatrix can be computed using only the vocalized frames. Notably, theaforementioned voice processing techniques are employed to identifyvoice segments of speech and calculate a feature matrix based on thesevoiced regions of speech. Voiced regions of speech can include phonemeswhich can be identified and located within the spoken utterance. Forexample, referring to FIG. 9, the speech recognizer 146, can identifyphonemes.

Following voiced activity analysis, Linear Prediction Coefficients (LPC)can be calculated from the energy bands of the perceptual filter bankanalysis. Pre-emphasis can be applied to the E_(V)(m,i) to reduce thedynamic range of the spectrum. This improves the numerical properties ofthe LPC analysis algorithms. The maximum of the amplitude spectrum isfound, and all points after the maximum can be multiplied by weightingcoefficients. The LPC's can then be converted to Line Spectral Paircoefficients (LSP's). Formants and anti-formants can be calculated fromthe LSP's, and a feature vector can be calculated from the formants andanti-formants. Upon determining the formants and anti-formants, afeature vector for each speech frame can be calculated. A feature matrixcan be created for the feature vectors representing voiced segments ofthe spoken utterance. The feature matrix can include formant locations,formant amplitudes, formant bandwidths, anti-formant locations,anti-formant amplitudes, anti-formant bandwidths, phase information,average amplitude information, difference information, and dynamicfeatures. In particular, the formant and anti-formant information isrepresented along a Bark scale. Differences in the formant andanti-formant information can be evaluated for characterizing one aspectof a natural change in a vocal tract configuration. That is, adistortion can be evaluated for one or more feature vectors foridentifying voice print matches generated from similar vocal tractconfigurations.

A vocal tract spectrum can be calculated from the feature matrix. Inparticular, formants having similar characteristics between the one ormore repetitions of the spoken utterance are used for creating the vocaltract spectrum. That is, formants substantially contributing to aconsistent representation of vocal structure are used for creating thevocal tract spectrum. The vocal tract spectrum can be calculated fromthe LPC's or from an autocorrelation function. Changes in the vocaltract shape, which correspond to a vocal tract configuration, can beidentified from changes in the vocal tract spectrum. In particular, thevocal tract configuration can be represented as one or more sectionshaving a corresponding length and area that are characteristic to one ormore sections of the user's vocal tract. A vocal tract configurationdifference corresponds to a bounded physical change of a user's vocaltract associated with one or more spoken utterances. For example, avocal tract configuration difference can be based on a momentum spectrumthat accounts for a dynamic change of a speech spectrum over time. Thedynamic change can occur to an amplitude of the spectrum or a phase ofthe spectrum. The momentum spectrum can include a lower bound and anupper bound for the one or more voice segments of speech, such thatvariations in the speech spectrum between the lower bound and the upperbound correspond to a unique vocal tract configuration. The upper andlower bounds for the feature matrix were presented in Table 1.

For example, referring to FIG. 9, the voice processor 944 calculates afeature matrix from the feature vectors for multiple sections of thespoken utterance corresponding to the one or more vocalized frames,wherein the feature matrix is a concatenation of feature vectors of theone or more vocalized frames. The voice processor 944 also normalizesthe feature matrix by removing vocalized frames shorter that apredetermined length and removing vocalized frames corresponding tovocal tract configurations that exceed an average vocal tractconfiguration. The vocal tract spectrum can be characterized orrepresented by a number of features in the feature matrix. Theattributes of the features have been selected from statistical researchof voice databases to minimize an intra-speaker variability and thatmaximize an inter-speaker variability.

Understandably, during voice authentication, the biometric voiceanalyzer (See FIG. 9) compares identification parameters of the featurevector against identification parameters of a stored feature vector ofthe speaker's voice. The parameters include the formant information andanti-formant information captured in the biometric voice print ofTable 1. Notably, the biometric voice print includes the three featurematrices (associated with the three repetitions of the phrase) and theattributes of Table 1 that characterize the user's vocal tract shape.That is, the vocal tract shape is characterized by, and can becalculated from, the feature matrix.

During calculation of the feature matrix for determining a vocal tractshape, a first vocal tract shape will be generated from the first threeformants specified in the feature matrix. The vocal tract shape curvecan be calculated with 0.2 cm increments from the formant frequencies. Avocal tract length can also be calculated for the voiced frames. Forexample, the biometric voice analyzer calculates a first vocal tractshape from lower formants of the first biometric voice print, determinesa vocal tract configuration difference based on the first vocal tractshape, identifies a similar vocal tract shape providing the smallestvocal tract configuration difference, and shapes the similar vocal tractshape from higher formants of the first biometric voice print. Thehigher formant frequencies are emphasized to characterize one aspect ofa speaker's articulation style.

Referring again to FIG. 9, the biometric voice analyzer 944 determinesone or more vocal tract cross-section areas from the feature vector, anddetermines one or more vocal tract lengths for the one or more vocaltract cross-section areas. Also, a communication bandwidth can be takeninto account when determining vocal tract shape. For example, formantfrequencies can be adjusted for telephone bandwidth which is generallybetween 140 Hz to 4.6 KHz: F1=640, F2=1730, F3=2860, and F4=3340. Thecross-section of the vocal tract can be updated based on the compensatedformant frequency locations. An average of the vocal tract cross-sectioncan be determined for the vocal tract shape based on one or morevocalized frames of speech. For example, the cross-section can bedetermined for phoneme regions of voiced speech where change in thevocal tract shape are relatively constant.

Variation bounds can be created based on a variability of the vocaltract shape for producing variation vectors for the feature vectors inthe feature matrix. For example, the biometric voice analyzer 944calculates a logarithmic distance for the variation vectors, andestablishes a threshold based on the logarithmic distance. The thresholdis used to determine whether a vocal tract configuration difference forauthenticating a user is within a variation bounds. The variation boundscan be represented as an average and a standard deviation of the featurevectors such as that shown in Table 1. The biometric voice analyzer 944also calculates a histogram on the variation bounds, determines amaximum for the histogram, calculates a derivative vector based on themaximum, and calculates a personal histogram and second variation boundsbased on the derivative vector.

During verification, biometric voice analyzer 944 evaluates a personalhistogram to determine whether a biometric voice print matches one ofthe said plurality of biometric voice prints for verifying an identityof the user. An identify is validated when a first plurality of bins ofthe personal histogram are filled, and wherein the identity isinvalidated when a second plurality of bins of the personal histogramare filled. Notably, the feature information of Table 1 in the biometricvoice print is used to generate a personal histogram for determiningwhen a user's vocal tract shape matches the personal histogram. Thehistogram statistically identifies whether the features of the biometricvoice print are characteristic of the person speaking. That is,variations in the speakers vocal tract shape can evaluated andstatistically compared to variations associated with a particular user'svocal tract configuration. Recall, multiple presentation of the spokenutterance are provided for determining a vocal tract configurationdifference; that is, a change in vocal tract shape. The personalhistogram provides a practical detection method for classifying andauthorizing a user. For example, during verification, the biometricvoice analyzer calculates a logarithmic distance, and evaluates athreshold for determining when the first plurality of bins of thepersonal histogram is filled. The threshold can also be adapted based onthe user's voice.

Benefits, other advantages, and solutions to problems have beendescribed above with regard to specific embodiments. However, thebenefits, advantages, solutions to problems, and any element(s) that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as a critical, required, or essentialfeature or element of any or all the claims. As used herein, the terms“comprises,” “comprising,” or any variation thereof, are intended tocover a non-exclusive inclusion, such that a process, method, article,or apparatus that comprises a list of elements does not include onlythose elements but may include other elements not expressly listed orinherent to such process, method, article, or apparatus. It is furtherunderstood that the use of relational terms, if any, such as first andsecond, top and bottom, and the like are used solely to distinguish onefrom another entity or action without necessarily requiring or implyingany actual such relationship or order between such entities or actions.

Where applicable, the present embodiments of the invention can berealized in hardware, software or a combination of hardware andsoftware. Any kind of computer system or other apparatus adapted forcarrying out the methods described herein are suitable. A typicalcombination of hardware and software can be a mobile communicationsdevice with a computer program that, when being loaded and executed, cancontrol the mobile communications device such that it carries out themethods described herein. Portions of the present method and system mayalso be embedded in a computer program product, which comprises all thefeatures enabling the implementation of the methods described herein andwhich when loaded in a computer system, is able to carry out thesemethods.

While the preferred embodiments of the invention have been illustratedand described, it will be clear that the embodiments of the invention isnot so limited. Numerous modifications, changes, variations,substitutions and equivalents will occur to those skilled in the artwithout departing from the spirit and scope of the present embodimentsof the invention as defined by the appended claims.

1. A method for speaker validation, comprising generating a first voiceprint in response to a speaker's voice; generating at least a secondvoice print in response to the speaker's voice; identifying a differencebetween the first voice print and the second voice print; determiningwhether the difference corresponds to a natural change of the speaker'svocal tract; and authenticating the speaker if the difference isindicative of a natural change in the speaker's vocal tract.
 2. Themethod of claim 1, wherein a natural change is a physical change of avocal tract that is correlated to changes in the speaker's articulatorygestures during a pronunciation of a spoken utterance and which areunique to the speaker.
 3. A system for generating a biometric voiceprint, comprising: a voice processor for receiving a spoken utteranceand at least one repetition of the spoken utterance from a user; abiometric voice analyzer for: calculating one or more vocal tract shapesfrom the spoken utterance and the at least one repetition, andcalculating a vocal tract configuration difference between the one ormore vocal tract shapes based on a varying pronunciation of the spokenutterance and the at least one repetition.
 4. The system of claim 3,wherein the voice processor: segments a spoken utterance into one ormore vocalized frames; generates one or more feature vectors from theone or more vocalized frames; calculates a feature matrix from the oneor more feature vectors; and normalizes the feature matrix over the oneor more vocalized frames.
 5. The system of claim 4, wherein the voiceprocessor further comprises a speech recognizer for: identifying voicedand unvoiced regions in the spoken utterance; recognizing one or morephonemes from the voiced regions; and identifying a location of the oneor more phonemes in the vocalized frames.
 6. The system of claim 4,wherein the voice processor generates the one or more feature vectors,by: segmenting a spoken utterance into one or more vocalized frames;performing a perceptual filter bank analysis on the one or morevocalized frames; calculating Linear Prediction Coefficients (LPC) fromthe perceptual filter bank analysis; converting the LPC's to LineSpectral Pair coefficients (LSP's); calculating formants andanti-formants from the LPS's; and creating a feature vector from theformants and anti-formants.
 7. The system of claim 6, wherein the voiceprocessor: calculates a feature matrix from the feature vectors formultiple sections of the spoken utterance corresponding to the one ormore vocalized frames, wherein the feature matrix is a concatenation offeature vectors of the one or more vocalized frames; and normalizes thefeature matrix by removing vocalized frames shorter that a predeterminedlength and removing vocalized frames corresponding to vocal tractconfigurations that exceed an average vocal tract configuration.
 8. Thesystem of claim 6, wherein the performing a perceptual filter bankanalysis includes estimating speech energy and noise energy in one ormore frequency bands along a Bark frequency scale.
 9. The system ofclaim 8, further comprising suppressing background noise during theperceptual filter bank analysis, by discarding filterbanks having aratio of speech energy to noise energy that do not exceed a threshold ofvocalization.
 10. The system of claim 6, wherein the calculating theLinear Prediction Coefficients includes applying pre-emphasis to thespeech signal.
 11. The system of claim 6, wherein the feature vectorincludes identification parameters that minimize an intra-speakervariability and that maximize an inter-speaker variability.
 12. Thesystem of claim 7, wherein the biometric voice analyzer: calculates oneor more vocal tract shapes from the feature matrix; calculates a vocaltract configuration difference from the one or more vocal tract shapes.evaluates a variability in the spectrum of one or more phonemes; andestablishes a variation bounds on a vocal tract configuration differencebased on the variability.
 13. The system of claim 12, wherein thebiometric voice analyzer compares identification parameters of thefeature vector against identification parameters of a stored featurevector of the speaker's voice.
 14. The system of claim 12, wherein thebiometric voice analyzer: determines one or more vocal tractcross-section areas from the feature vector; and determines one or morevocal tract lengths for the one or more vocal tract cross-section areas.15. The system of claim 14, wherein the biometric voice analyzer:calculates a variation bounds for producing variation vectors for thefeature vectors in the feature matrix; determines a logarithmic distancefor the variation vectors; and establishes a threshold based on thelogarithmic distance, wherein the threshold is used to determine whethera vocal tract configuration difference for authenticating a user iswithin a variation bounds.
 16. The system of claim 15, wherein thevariation bounds is represented as an average and a standard deviationof the feature vectors.
 17. The system of claim 15, wherein thebiometric voice analyzer: determines a variation bounds on the personalvocal tract shape; calculates a histogram on the variation bounds;determines a maximum for the histogram; calculates a derivative vectorbased on the maximum; calculates a personal histogram and secondvariation bounds based on the derivative vector.
 18. The system of claim17, wherein the biometric voice analyzer evaluates a personal histogramto determine whether a biometric voice print matches one of the saidplurality of biometric voice prints for verifying an identity of theuser, wherein the identify is validated when a first plurality of binsof the personal histogram are filled, and wherein the identity isinvalidated when a second plurality of bins of the personal histogramare filled.
 19. The system of claim 18, wherein the biometric voiceanalyzer calculates a logarithmic distance; and evaluates a thresholdfor determining when the first plurality of bins of the personalhistogram is filled.
 20. The system of claim 19, wherein the thresholdis adapted based on a user's voice.
 21. The system of claim 19, furthercomprising an Applications Programming Interface (API) having: abiometric voice print creation module, a pass phrase creation module;and a device identifier module, for creating a user profile thatincludes a biometric voice print, a pass phrase, and a device identifiergenerated from the biometric voice print creation module, the passphrase creation module, and the device identifier module.
 22. A methodfor voice authentication, comprising: determining one or more vocaltract shapes from one or more received spoken utterances from a user;evaluating a vocal tract difference between the one or more vocal tractshapes; comparing said vocal tract difference against a storedrepresentation of a reference vocal tract shape of the user's voice; anddetermining whether the vocal tract configuration difference isindicative of natural changes to the reference vocal tract shape,wherein natural changes are variations in the vocal tract configurationwhich can be physically articulated by the user.
 23. The method of claim22, wherein determining one or more vocal tract shapes furthercomprises: calculating a first vocal tract shape from lower formants ofthe first biometric voice print; determining a vocal tract configurationdifference based on the first vocal tract shape; identifying a similarvocal tract shape providing the smallest vocal tract configurationdifference; and shaping the similar vocal tract shape from higherformants of the first biometric voice print.
 24. The method of claim 22,further comprising: determining a source of said spoken utterance,wherein the source is one of the user speaking said spoken utteranceinto a microphone or a device playing back a recording of the spokenutterance into the microphone; and granting access if the source is theuser, and not granting access if the source is the device.
 25. Themethod of claim 24, wherein the determining a source further comprises:identifying whether an acoustic signal representing the spoken utteranceis characteristic of a waveform produced by a digital recording device,wherein the identifying includes recognizing a spectral tilt imparted bysaid digital recording device.